1

Demand and revenue implications of an integrated public transport policy. Thecase of Madrid

Anna Matas Departament d’Economia Aplicada, Universitat Autònoma de Barcelona,

08193 Bellaterra, Barcelona, Spain. Phone: +34.93.581.15.78; fax: +34.93.581.22.92; e-

mail: anna.matas@uab.es

ABSTRACT

One of the most popular options for promoting public transport use is the provision of

an integrated and high quality public transport system. This was the strategy adopted by

the regional government in Madrid in 1986 and since then public transport patronage

has increased by more than 50%. This paper has two objectives. The first is to identify

the factors underlying the significant increase in the demand for public transport in

Madrid. To do this we estimate an aggregate demand function for bus and underground

trips, which allows us to obtain the demand elasticities with respect to the main

attributes of public transport services and also to calculate the long-term impact of

changes in those explanatory variables on patronage. The second objective is to evaluate

the impact on revenue derived from the introduction of the travel card scheme, and to

discuss the consequences on revenue of changes in the relative fare levels of different

types of ticket without substantially affecting patronage. This latter issue is addressed

by estimating a matrix of own and cross-price elasticities for different ticket types.

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1. Introduction

In most urban areas, it is increasingly difficult for public transport to compete with the

private car. An increase in real income has resulted in a higher level of car ownership

and use, and the ongoing process of residential suburbanisation and employment

decentralisation experienced in the largest cities has generated a pattern of travel that is

more suited to the private car than to public transport. The changes observed in urban

travel can be summarised by a growing number of motorised trips as well as an increase

in the use of the private car.

In order to reverse this trend, local authorities have implemented several strategies to

promote public transport use. The supply of an integrated and high-quality public

transport system has been one of the most favoured options. Clear examples of such a

policy are the integrated regional public transport systems in Germany, Austria and

Switzerland documented in Pucher and Kurth (1996). Integration is a broad concept that

includes several issues, such as the co-ordination of service levels, routes and

timetables, and a common fare system. This is the strategy adopted by the regional

government of Madrid with the main aim of promoting public transport use and shifting

demand away from the private car. Since 1986, passenger transport infrastructure and

services have been planned as a complete system. The most significant change was the

creation in 1987 of an integrated fare system for the whole public transport network

based on a travel card. At the same time, the quality of public transport has been

improved, mainly through the extension of bus, underground and suburban rail

networks. The number of passengers using public transport services has grown from

951 million in 1986 to 1549 million in 2001.

This paper has two objectives. The first is to identify the underlying factors behind the

significant increase in public transport demand in Madrid. To do this, we estimate an

aggregate demand function, which allows us to obtain the demand elasticities with

respect to the main attributes of public transport services and additionally to calculate

the long-term impact on patronage of changes in the explanatory variables. The second

objective is to evaluate the impact on revenue derived from the introduction of the travel

card scheme and to discuss the role of possible changes in relative fare levels for

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different ticket types in order to increase revenue without essentially affecting

patronage. In fact, one of the most important concerns with the travel card is its negative

impact on revenue. Theoretical and empirical work on this topic (for instance, White

1981, Doxsey 1984, Carbajo 1988) shows that the final impact will depend on the

combination of fare levels for different ticket types, and on user distribution according

to rates of use. On this matter, Carbajo (1988) concludes that the potential gains of non-

uniform pricing will only be attained if operators depart from arbitrary pricing rules

when setting fares for the different ticket types. In order to provide useful information

about potential changes in fare structure which reduce the negative impact on revenue,

we have estimated a matrix of own and cross-price elasticities for different ticket types.

This paper is structured as follows: section 2 provides an overview of supply and

demand in the region of Madrid, section 3 explains the methodology and the data set

while section 4 presents the estimation results of the aggregate demand model and the

long-term effects of the explanatory variables. Section 5 is devoted, firstly, to the

evaluation of the impact of the travel card on revenues and secondly to the estimation of

demand elasticities with respect to ticket type. Finally, section 6 summarises the main

conclusions.

2. Supply and demand of public transport in Madrid: an overview

The region of Madrid has more than five million inhabitants and is made up of 179

municipalities. About 56% of the people live in the city of Madrid and 44% in the rest

of the region. In recent decades, there has been a clear process of suburbanisation; since

1975 the population of the city of Madrid has decreased, while that of the rest of the

region has increased. The employment structure is dominated by the service sector,

which accounts for 75% of total jobs (of which 35% are in the public sector). This

dominance of services implies that so far jobs have remained heavily centralised in the

city of Madrid. According to different estimations, around 70% of the jobs are located

in the city and most of them in the city centre. The distribution of residential areas and

employment centres gives rise to an urban structure that predominantly follows a

monocentric model with radial trips from satellite settlements to the city centre. This

structure makes Madrid well suited to public transport use.

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The public transport supply in the Madrid region is summarised in table 1. Since 1986

the Consorcio de Transportes de Madrid (CTM), a regional public authority, has co-

ordinated the services, networks and fares of the different modes of transport operating

in the area. The underground company and the urban bus company (EMT) mainly

operate within the boundaries of the Madrid municipality. The suburban railway and

357 lines of suburban buses serve the metropolitan area. Since its creation, CTM has

been engaged in an active policy of improving the quality of public transport. The most

relevant measures include the enlargement of the metro network to the outskirts as well

as the improvement of its structure and connections, extension of the urban bus service

network, together with better vehicles, significant improvements in the quality of the

suburban railway, with more frequent and better trains, and finally an improvement in

metropolitan buses and underground connections.

Table 1. Public transport supply and demand in the region of Madrid. 2001

Operators Route length (km) Vehicle*km (106) Trips (106)Underground 171 122.4 543.0Urban buses (EMT) 1531 95.9 544.7Suburban rail 311 86.7* 176.5Suburban buses 3345 135.9* 284.9* Data for 2000Source: Consorcio Transportes de Madrid

The annual number of trips is split among the different modes of transport as follows:

underground 35%, urban bus 35%, rail 11% and suburban buses 19%. Unfortunately, it

is not possible to obtain a homogenous time series for the suburban railways and buses.

That is why the demand analysis will be based hereafter on data from the two main

transport companies, underground and urban buses, which account for 70% of the

market.

The trend in urban public transport patronage is shown in figure 1. Since the mid

seventies until 1985 the number of passengers decreased continuously, with patronage

falling from 927 million trips in 1975 to 763 million in 1985. This decrease has been

much more significant for the underground. The increase in bus passengers between

1979 and 1981 can be explained by the fact that the urban bus company (EMT) took

over several private bus companies operating in the area. In sharp contrast to this, from

1986 until the present the trend has been reversed and patronage rose to 1088 million

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trips in 2001. Therefore, a remarkable increase in public transport use of more than 40%

was observed. The turning point in demand coincides with the creation of the CTM but

also with the recovery of the Spanish economy from a severe economic crisis. A

significant part of this increase is attributed to the development in 1987 of an integrated

fare scheme covering all modes of public transport in the region. The system is

essentially based on a monthly travel card that permits unlimited travel across the entire

network. Prices are set according to a zonal structure based on concentric rings around

the central area1. After the introduction of the new fare scheme, passengers could

choose between three different ticket types: ordinary tickets, multi-ride (ten-trip) tickets

and a travel card2.

The economic effects of travel passes have received substantial attention in the transport

literature. With respect to the effects on passengers, (our main variable of interest), most

studies agree that the introduction of a differentiated price scheme provides benefits to

users3 such as fare discounts (the average cost per trip is lower), reduction of

interchange penalties and reduction in boarding and queuing time. Also, once the pass is

purchased, it permits travel at a zero marginal cost. Therefore, the most likely impacts

1 A more detailed explanation of this system is found in appendix 1.2 In appendix 2, the fare structure for bus and underground services is explained in more detail.3 White (1981) presents a detailed enumeration of the benefits of travel passes to operators, passengers,local authorities and employers.

300000

350000

400000

450000

500000

550000

70 75 80 85 90 95 00

Underground Urban bus

Figure 1. Trips by public transport in Madrid (thousands)

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of the travel card will be an increase in the number of trips made by previous travellers

and a high probability of attracting new users. On the other hand, substitution of other

ticket types may also be observed. Frequent users will change from single tickets to

travel cards as soon as they find them worthwhile.

A preliminary analysis of data in Madrid shows that patronage has increased at an

annual rate of 2.2% whereas significant changes in the share of each ticket type have

occurred (table 2).

Table 2. Demand composition by ticket type. 2001

Bus Underground

1987 2001 1987 2001Single 27.4% 4.3% 47.7% 4.5%Multi-ride 56.8% 23.8% 36.7% 35.0%Travel card 15.7% 71.9% 15.5% 60.4% Ordinary - 43.1% - 47.0% Young - 14.1% - 9.2% Elder - 14.7% - 4.2%Source: Consorcio Transportes de Madrid

The use of single tickets has fallen to just over 4% for both modes of transport. It can

also be observed that, the market share of multi-ride tickets among bus users dropped

from 57% to 24% whereas in the case of the underground it has remained almost

constant. This difference can mainly be explained by the fact that bus transfers are

penalised in monetary terms whereas underground transfers are free; this implies that

the break-even ratio between multi-ride ticket and travel card is lower for bus users. The

market share for travel cards in 2001 exceeded 60%, which was the target figure when

the integrated fare system was introduced. It is also interesting to note that there is a

higher proportion of concessionary trips among bus users than among underground

users. Such an extensive use of the travel card is not surprising. Following White's

(1981) arguments, in those areas where flat fares are applied, as soon as a travel pass is

attractive to the regular passenger a large proportion of users will also find purchasing it

worthwhile. The market share of travel cards in Madrid is comparable with that of other

cities in which there is a long tradition of travel passes. In Stockholm, travel passes

represent on average 82% of all trips (Tegner et al. 1998), while there are figures of

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92% in Vienna, 78% in Hamburg, 78% in Rhein-Ruhr, 54% in Zurich and 50% in

Munich (Pucher and Kurt 1996).

In 2001, more than 13 million monthly travel passes were sold, of which 63%

corresponded to ordinary holders, 16% to young holders and 21% to senior citizens4.

Use of travel cards valid for two or more zones has increased at a higher rate than for

those valid only for the central zone. Figure 2 shows that from 1987 to 1990 both series

behave similarly, but after this date, the use of travel cards for zones B and C increased

more sharply. This increase is explained by several factors, three of which are; firstly,

the process of residential suburbanisation in the Madrid region; secondly, the

improvement in the quality of the suburban rail and suburban bus services; and thirdly,

the economical incentive that the travel card entails for long distance travellers. Travel

cards valid for zones B and C are mainly used for radial trips, Madrid being the major

attraction centre5.

4 In order to calculate the total number of cards sold, the annual cards are converted into monthly cards.5 For a review of the characteristics and effects on mobility of the tariff integration policy see Echevarría-Jadraque and Martínez-Sanchez (1997).

0

1000000

2000000

3000000

4000000

5000000

6000000

7000000

87 88 89 90 91 92 93 94 95 96 97 98 99 00 01

CARDS ZONE A CARDS ZONES B-C

Figure 2. Number of monthly cards according to the zones

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3. The model and data

3.1. Model specification

To explain the increase in public transport patronage in the city of Madrid, a demand

model for bus and underground services is estimated. Two different equations are

estimated, allowing for substitution effects between the underground and the bus.

Ideally, in order to properly account for relationships of complementarity and

substitutability, a complete system of demand equations should be estimated.

Unfortunately, the lack of data on car usage makes such an analysis impossible.

This study relies on time series data. Therefore, the estimated equations should be

interpreted as aggregate demand functions. It is well known that the aggregate demand

function is based on certain restrictive assumptions about consumer behaviour.

Nevertheless, it allows for an approximation of the behaviour of an aggregate group of

individuals and is useful for given purposes. In our case, the estimation of an aggregate

demand equation allows us to offer an approximation of the underlying factors behind

changes in transport demand and to the corresponding demand elasticities.

The demand for transport services can be assumed to depend on the attributes of each

mode, the competing modes of transport and on the socio-economic characteristics of

the population. The competing modes considered as feasible transport alternatives in the

city of Madrid are bus, underground and car6. The relevant characteristics that define

the modes of transport are basically monetary costs and quality of service variables.

Monetary cost for public transport modes has been approximated by a fare index

explained below in section 3.2. In order to estimate cross-price elasticities between

transport modes, both bus and underground fares should be included as explanatory

variables in each demand equation. However, the evolution of the changes in bus and

underground fares was increasingly similar over the period 1981-1989 and has been

exactly the same since 1990. The high correlation index for these two variables (0.97)

6 For those travelling outside the city of Madrid, suburban rail and suburban buses become relevantoptions. Unfortunately, no data is available for supply characteristics of these modes of transport for along enough period.

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has precluded including both variables in the equation. Therefore, only the own mode

fare enters the equation and elasticities should be interpreted as conditional (for equi-

proportional fare changes). Monetary cost of car use is approximated by the price of

petrol. All monetary costs are deflated by the Retail Price Index (RPI).

Service quality variables are limited by the aggregate nature of the data and by the time

period considered. In this study, service quality is captured by route length for both the

bus and the underground. Alternatively, the commonly used measure of vehicle-

kilometres was tested. However, it was discarded because from 1982 until 1989 the bus

company undertook a process of route rationalisation with the aim of reducing spare bus

kilometres without essentially affecting the quality of the service. Additionally, other

variables such as the average speed of buses and average frequency were tested but

none of them showed to be significant and were therefore omitted.

A dummy variable was included to take into account the full development of the

integrated fare scheme. As explained in appendix 1, the travel card was first introduced

in 1987 and subsequently expanded. In 1988 concessionary passes and annual travel

passes were created and in 1989 the system was extended to its present limits.

Accordingly, a step dummy variable was introduced that takes value 0 before 1987, 0.33

in 1987, 0.66 in 1988 and 1 thereafter.

Transport demand depends on the socio-economic and demographic characteristics of

the area. The variables considered are: population, suburbanisation index, employment

level, income and car ownership. Due to the lack of data on regional household

disposable income before 1986, this variable has been approximated by real GDP

(Gross Domestic Product) in the Madrid region. Although underground and urban bus

services operate mainly in the city of Madrid, the catchment area is the Madrid region.

For this reason, all variables refer to a regional level. As expected, there is a high

correlation between GDP and car ownership (0.99). In fact, car ownership positively

depends on income per capita. Our decision was to include only GDP as an explanatory

variable, bearing in mind that it expresses the effects of both variables; a positive effect

which reflects the increase in activities and therefore mobility, and a negative effect of

an increase in car ownership.

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The demand equations can be expressed as follows:

),,,,,,( , tttttittitit gespdrcfhY = (1)

where,

i= bus, underground

Yit=passenger trips in mode i and period t

fit=fare index in mode i and in period t

ct= private car fuel costs in period t

rit=route length in mode i and period t

d t=dummy variable that reflects the change in fare structure

pt=population in period t

s t=suburbanisation index in period t

et=employment level in period t

gt=real GDP in period t

The demand equations are estimated using a double log specification. As is well known,

this functional form is often criticised for the constant elasticity assumption7. However,

the double log form has the advantage of permitting a clear interpretation of the

corresponding coefficients in terms of elasticities. The assumption of the constant

elasticity model was tested by the estimation of both a linear specification and a semi-

log specification. The selection among these alternative functional forms was based on

their goodness of fit. The results showed that the double-log was statistically the best

option8. Additionally, the reliability of the selected model was evaluated according to

the usual econometric practice, testing the lack of disturbance autocorrelation (if the

functional form were mis-specified this would be manifested in autocorrelation of the

disturbances) and the structural constancy of coefficients, using recursive estimation

techniques. On this basis, the model is not rejected by the data. Therefore, we consider

that the double logarithmic specification offers an adequate approximation, at least in

the neighbourhood of actual data.

7 Additionally, the adding-up restriction is not fulfilled by the double-log specification. However, thisproblem is less severe when it does not apply to a complete system of equations. In this case, the double-log functional form can be defended at least for a restricted range of data.8 The criterion used was based on the likelihood function according to the Box-Cox (1962) suggestion.

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The demand equation is estimated in levels9. To allow for dynamic effects, the starting

specification includes lags of the dependent and the explanatory variables. The search

for the final specification followed a general to specific process. After simplifying the

model with restrictions that were not rejected by the data, a partial adjustment equation

was selected. Lagged explanatory variables were not significant and do not appear in the

final model. The equations to be estimated can therefore be expressed as follows:

itititi

tititititititiitiit

YgespdrrcfY

εγβββββββββα

++++++++++++=

− )ln()ln()ln()ln()ln()ln()ln()ln()ln()ln()ln(

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8765241321

(2)

3.2. The data

Demand equations are estimated using annual data for the period from 1979-2001.

Although passenger data has been compiled since 1970, the breaks in the series in the

years leading up to 1979 preclude the possibility of extending the analysis10. Demand

has been measured as the number of registered trips. A trip is registered every time a

mode of transport is boarded and so two links of the same journey are counted as two

different trips. This is so except for transfers between underground lines. Data on

alternative and more consistent measures of demand is not available11. Table 3 shows

the percentage variation in bus and underground passenger trips that has already been

commented in section 2.

9 In order to select between estimating in levels or in differences, we estimate the static equation in levelsand given that the D.W. statistic showes no autocorrelation among the error terms we proceed with thedemand model specified in levels. Given the low number of available observations the application offormal unit roots and cointegration test is not advisable.10 Different corrections need to be made to the passenger data in order to construct a homogenous timeseries. The most important one is the correction of the effect of strikes in 1990, 1991 and 1992 accordingto the companies’ own estimations.11 The way trips are registered inflates the number of passengers using buses with respect to those usingthe underground. The use of the total number of trips as the measure of demand can introduce distortionsin the historical time series if the total number of transfers per trip has changed over time. This might wellbe the case when demand shifts from single tickets to travel cards.

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Table 3. Percentage growth of the variables in the model

1979-1986 1986-2001 1979-2001

Trips Bus 7.2 23.6 32.5 Metro -16.0 65.0 38.6Fare index Bus 19.7 21.0 44.8 Metro 73.5 19.2 106.8Route length Bus 17.9 21.9 43.8 Metro 57.4 57.4 147.8Vehicle-km Bus - 15.2* - Metro 11.6 51.8 69.5Fuel price -10.9 -5.0 -15.3Population 4.1 12.4 17.0Suburbanisation 19.5 24.8 49.1Employment 0.0 50.2 50.2Real GDP 22.5 62.9 100.3* 1989-2001

An aggregate price index has been constructed trying to properly reflect both changes in

fare levels and structure. This is not a straightforward task when several ticket types are

in use. The alternative chosen here is similar to that used by London Transport (1993).

The fare index is a weighted average of the changes in individual ticket prices, where

the weights used are the number of trips made using each ticket in the base period. This

index measures the changes in fares that would result if people’s ticket buying

behaviour were unchanged after the revision. A problem arises when a new ticket type

is introduced, as was the case in 1987 with the introduction of the travel card scheme.

The solution adopted was to use the change in average revenue as an approximate

measure of the fare change in that particular year12. According to this index, real fares

increased markedly over the whole period and particularly for the underground between

1979 and 1986. It has to be mentioned that the fare’s level in the starting year was very

low. Moreover, since 1987, frequent users could switch to the travel card, which

permitted travel at a lower price per trip. The noticeable increase in bus and mainly

underground route length shows the active policy followed to promote public transport.

12 Different alternative solutions were tried with no significant effect on the estimated coefficients of thedemand equation.

13

The population in the Madrid region has risen at an annual rate of 0.7%, although its

growth rate is higher after 1986. This growth is the result of a steady decrease in the

population of the city of Madrid, which is compensated by a larger increase in the rest

of the region. This change in residential population is reflected by the continuous

increase of the suburbanisation index, which is the ratio of population in the rest of the

region to the total population. Annual increase in GDP was lower in the first period,

although economic recovery started in 1985. Finally, with respect to the employment

level it has to be noted that there has been a significant increase since 1986, after 11

years of severe economic crisis. Figure 3 shows the clear synchronisation between

employment and the demand for the underground with a correlation coefficient equal to

0.96.

4. Estimation and results

4.1. Demand equations and elasticities

The equation system has been estimated by applying the seemingly unrelated regression

(SUR) method, instead of ordinary least squares (OLS), in order to take into account

contemporaneous correlation in the errors across equations and therefore to increase

efficiency. It is very likely that some factors not included in the equation may affect

300000

350000

400000

450000

500000

550000

1200000

1400000

1600000

1800000

2000000

2200000

80 82 84 86 88 90 92 94 96 98 00

Passenger trips, left scale Employment, rigth scale

Figure 3. Underground passenger trips (thousands) and employment in Madrid region

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both bus and underground patronage. It therefore seems reasonable to allow correlation

of the disturbance across modes of transport.

The general model estimated according to equation 2 is presented in table A.1 in the

appendix. In the bus equation, the coefficients related to the characteristics of the modes

of transport have the expected sign and have been estimated with a high degree of

reliability. In the underground equation only its own attributes appear as significant. In

both equations the coefficients of the socio-economic variables are estimated with high

standard errors (as a result of the high degree of multicollinearity observed among

them13 and the relatively small sample size) and none of them appear to be clearly

significant. In order to increase the efficiency of the estimates we proceed by excluding

some of the non-significant variables from the equation. We should consider that some

of these variables express the same phenomenon. For instance, given that demand is

measured as the total number of trips, the population and GDP variables express the

region’s growth. Although from a statistical point of view the population coefficient has

a slightly higher degree of significance, from a conceptual point of view, GDP is a more

relevant variable. Therefore, on those grounds we decided to exclude population and

maintain GDP in the demand equations. In the restricted model, the socio-economic

variables that appear to be significant in the bus equation are GDP and the

suburbanization index whereas the level of employment is the key economic variable

explaining underground demand. The hypothesis of equal GDP elasticity for bus and

underground trips was not rejected by the data and so the model was estimated in

accordance with this assumption. The results of the restricted model are presented in

table 414. It has to be emphasised that moving from the general to the restricted model

does not significantly affect the coefficient of the transport variables, our main variables

of interest, while their estimated variance is smaller.

13 The correlation matrix is:

GDP Population Suburbanization EmploymentGDP 1.000 0.984 0.975 0.963Population 0.984 1.000 0.970 0.947Suburbanization 0.975 0.970 1.000 0.895Employment 0.963 0.947 0.895 1.000

14 According to the likelihood ratio test the restricted model was not rejected by the data; the calculatedchi-square was 13.09, while the tabulated value for 7 degrees of freedom at 5% significant level is 14.1.

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Table 4. Estimation results of demand equations for bus and underground. 1979-2001

Dependent variable: log (bus trips) log (underground trips)

Explanatory variables Coefficient t-statistic Coefficient t-statistic

Constant term 6.528 5.73 5.152 10.89Log (bus fare) -0.208 -3.52 n.a.Log (underground fare) n.a. -0.369 -9.44Log (bus network) 0.479 6.26 n.s.Log (underground network) -0.210 -7.96 0.245 5.82Log (petrol price) 0.155 6.46 n.s.Log (bus trips (-1)) 0.298 4.28 n.a.Log (underground trips (-1)) n.a. 0.399 8.30Log (GDP) 0.148 2.74 0.148 2.74Log (employment) n.s. 0.305 3.18Log (suburbanisation) 0.397 2.71 n.s.Step dummy variable 0.034 2.74 0.053 4.07

Number of observations 23 23R-square 0.992 0.994Adjusted R-square 0.987 0.992S.E. regression 0.009 0.012h-test -0.22 -0.69

n.a. not applicablen.s. not significant

The estimated coefficients are highly significant, the level of fit is high and no

correlation is detected in the disturbances according to Durbin’s h statistic. Given the

double-log specification, the estimated coefficients can be interpreted as short run

elasticities and, as can be observed, they are in line with the standard literature15. In our

study, short run is defined as the effect on demand occurring within one year of a

change in the relevant variable. Bus fare elasticity is equal to –0.21. This value is

similar to that estimated by Dargay and Hanly (2002) for English Metropolitan areas

(excluding London). The underground’s fare elasticity equals to –0.37; the evidence in

this case is sparser but reviewed papers show a range of –0.2 to –0.416. On average,

underground users appear to be more price-sensitive than bus users. The explanation for

this result might be the higher market share of travel cards in the bus market, which, as

is argued in section 5.2, appear to be the least sensitive to price. The elasticity of bus

demand with respect to the price of petrol is equal to 0.16 and is clearly significant.

However, the price of petrol has no significant effect on underground patronage.

15 See Oum et al. 1992, Goodwin, 1992, Nijkamp and Pepping 1998 and Dargay and Hanly 1999.16 Gilbert and Jalilian 1991, Oum et al. 1992, London Transport 1993.

16

Service quality measured in terms of route length is found to have a positive impact on

public transport patronage, although the effect on the bus market doubles that of the

underground17. Given that in aggregate studies a very crude proxy for the quality of

service is used, it is difficult to give an adequate interpretation of the estimated

elasticities and, therefore, evidence of service elasticity is less conclusive. It is

interesting to note that bus users are sensitive to the quality of the underground service,

while there is no evidence of the reverse effect. This result suggests that the

underground is a relevant option for bus users but not vice versa. This might well be the

case for commuters who place a higher value on time costs and reliability, in which case

the underground is a better option.

The coefficient estimated for the dummy variables that capture the non-pecuniary

effects of the travel card indicates a growth in bus and underground patronage of 3.4%

and 5.3% respectively. These figures proved highly robust under different specifications

of the dummy variables. The higher percentage for the underground can partially be

explained by the introduction in 1987 of a multi-ride ticket at an advantageous price,

while this ticket has only been a feasible option for the bus since 1979.

According to the dynamic structure of demand, the long-term effects are around 1.4

times those of the short-term for the bus and 1.7 times for the underground. Again the

values found agree with other empirical evidence. The period of adjustment is relatively

short, with about 60% of the effects experienced within the first year and 95%

completed within three years.

Income elasticity is positive and equal to 0.15. This value agrees with the values found

by Asensio et al (2003) for a range of Spanish cities. These authors show that in small

cities, where the relative quality of public transport tends to be worse, car ownership

levels are more income elastic, and public transport services have characteristics of

inferior good. On the contrary, in larger cities, where the relative quality of public

transport is usually better, public transport is regarded as a substitute for private

transport by a larger share of population and its use is less sensitive to changes in

income. Nonetheless, our finding differs from the negative income elasticity found in

17 It has to be noted that when in the underground equation the quality of service was measured in termsof vehicles-kilometre, the estimated elasticity was nearly identical.

17

other studies, for example, in the UK18. One possible explanation for this difference is

the higher population density of Spanish cities, which makes them better suited to

public transport use than to car use. This argument is suggested in Webster et al (1986),

who verify that “in Spanish towns public transport is used on average a little more than

in British towns, but in car-owning households the rate of public transport use is nearly

half as much again as in Britain “.

The level of employment appears to be a significant variable to explain underground

demand but not bus demand. This result confirms what was shown in figure 3 and

suggests that commuters represent a higher proportion of users in the case of the

underground than in that of buses. The role that employment plays in the demand

equation in Madrid is similar to the one found by London Transport (1993). The

estimation of a demand equation for bus and underground services in London showed

that employment was a significant explanatory variable in the underground equation but

did not explain changes in bus demand.

4.2. Long-term impact on patronage of changes in explanatory variables

As stated earlier, one of the objectives of this study was to quantify the causes of the

significant increase in public transport use in the region of Madrid since 1986. The

estimated equations allow us to disentangle the effect on patronage as a result of the

applied transport policy (fares and service quality) from the effect of other exogenous

variables. Taking into account the dynamic effects, we have evaluated the change in

patronage from 1986 to 2001 that can be attributed to the change in each explanatory

variable19. Table 5 shows the change in the explanatory variables and the corresponding

impact on patronage.

18 See, for instance, Dargay and Hanly (1999).19 In order to calculate the impact on demand that can be attributed to variable X we used the followingprocedure. First, from the demand equation, the total number of trips in 2001 has been forecast under theassumption that variable X has remained constant all over the period and equal to its value in 1986.Second, the contribution of variable X to the change in the number of trips has been calculated as thedifference between the observed number of trips in 2001 and the predicted number of trips assumingvariable X has remained constant. This procedure has been repeated for each variable in the model.

18

Table 5 . Estimated effect on patronage of changes in explanatory variables 1986-2001

(percentage change)

Bus marketExplanatory Variable Percent change in

the explanatoryvariable

Impact on patronage (%)

Fare index 21.0 -5.5Bus route length 21.9 16.3Underground route length 57.4 -16.1Petrol price -5.0 -0.4Travel card - 7.1GDP 51.8 12.8Suburbanisation 24.8 15.4

Underground marketExplanatory Variable Percent change in

the explanatoryvariable

Impact on patronage (%)

Fare index 19.2 -15.8Underground route length 57.4 27.1Travel card - 14.9GDP 51.8 18.7Employment 50.2 29.3

As far as the bus market is concerned, our research shows that the growth in real fares

has reduced demand by more than 5%. Secondly, it can be observed that the extension

of the bus network has had a significant impact on demand, which has, however, been

practically offset by the enlargement of the underground network. The final effect of the

introduction of the travel card is slightly higher than 7%. The impact on patronage of

the two exogenous variables included in the equation has proved to be positive. Real

GDP growth leads to an increase of nearly 13% of total trips while suburbanisation has

expanded demand by an additional 15%. The impact of the last variable has to be

interpreted with caution. As was explained in section 2, the largest increase in travel

coupons corresponds to those valid for two or more zones; that is, in users travelling

from the rest of the region to the city of Madrid (or vice versa). However, this growth

has not occurred without the influence of an integrated fare system. Hence, it is possible

that the impact of suburbanisation reflects part of what is really due to the impact of the

travel card. It was not possible to further disentangle the effects of these two variables.

In relation to the underground, the long-term effect of underground extensions and the

travel card are 27% and 15% respectively, which have been partially offset by the

increase in real fares. Employment and GDP growth have contributed by 29% and 19%,

respectively, to the increase in patronage.

19

The impact of the travel card on patronage falls in the range of values reported by other

studies. FitzRoy and Smith (1998), estimate that the introduction of two heavily

discounted travel cards in the city of Freiburg increased the number of bus trips per

capita by 9.4% and 13.9%, respectively. FitzRoy and Smith (1999) report that the

impact of the introduction of a season ticket scheme in four Swiss cities on the total

number of bus passenger trips had the following results: Geneva, 16%, Bern 14%, Basel

5,4% and Zurich 4,5%. However, the impact of the travel card in Madrid is below that

estimated for London. London Transport (1993) quantifies the impact on patronage of

the May 1983 fare revision and introduction of the Travelcard as a growth of 33% for

underground passenger-miles and 20% for bus passenger-miles from 1982/83 to

1990/91.

5. Impact on revenue and price elasticity with respect to different ticket types

5.1. Impact on revenue

In this section, we want to evaluate the consequences for revenue derived from the

changes in the level and structure of the fare system. An analysis of the related literature

leads to the conclusion that the implementation of a non-uniform price scheme does not

necessarily imply an increase in financial support. From his theoretical analysis,

Carbajo (1988) concludes that the effect of different combinations of fares on revenue

will depend on how users are distributed according to their intensity of use. Doxey

(1984), working under the assumptions of price-inelastic demand and wide dispersion of

individual demand at any fare level, concludes that travel pass sales will have a negative

impact on revenue. However, as FitzRoy and Smith (1998) state, Doxey's argument

ignores the potential generation effect of travel cards. On the other hand, White (1981)

argues that although a travel card pricing policy does not necessarily imply an increase

in deficit in the long run, the common policy followed in the short-term of setting a low

price for travel cards in order to achieve high market penetration involves an increase in

financial support. This result seems to be confirmed by empirical evidence. Pucher and

Kurth (1996) conclude that for five case studies analysed (Hamburg, Munich, Rhein-

Ruhr region, Vienna and Zurich) the service improvements and the integrated fare

system required substantial government subsidies. On the other hand, FitzRoy and

20

Smith (1998) show that the low cost travel cards introduced in Freiburg have not

deteriorated the operating deficit of the transport company. Nonetheless, the authors

suggest that this outcome is perhaps exceptional. Similarly, LT (1993) presents a

significant positive effect of travel cards on underground revenue and a smaller positive

effect on bus revenue, due to the large increase in patronage.

For the two municipal companies that operate bus and underground services in Madrid

we have calculated the effect of the change in ticket types on revenue by breaking down

the components of total revenue. The results are presented in table 6 in percentage

terms. For the bus company, revenue has increased at a rate lower than the inflation rate,

patronage and the real fare index have had a positive effect, while the increase in the

market share of ticket pass holders has clearly reduced revenue. According to the results

presented in table 5, this reduction has been weakly offset by the generation effect due

to cheap travel passes. The corresponding figures for the underground company show a

significant increase in total revenue in real terms, which is mainly explained by the

increase in the total number of passengers. Again, the travel pass has had a negative

impact on revenue only partially offset by the attraction of new users by the travel cards.

It should be remembered that the extension of the underground network explains a

significant increase in demand with the corresponding effect on total costs. In order to

find the net financial effect of the transport policy applied since 1986 it would be

necessary to calculate the impact on total costs and, therefore, on operating deficit.

However, this task goes beyond the scope of this study.

Table 6. Impact of travel card on company revenues. 1986-2000

Revenues Inflation Passengers Real fares index Change in tickettypes

BUS 78.4% 83.8% 18.9% 16.9% -35.3%UNDERGROUND 124.2% 83.8% 58.8% 14.6% -38.2%

5.2. Price elasticity with respect to ticket type

The change in fare structure has resulted in a negative impact on revenue, which might

not be sustainable in the long run. According to the aforementioned authors, it is

possible to find a combination of fare levels for different ticket types that reduces this

21

negative effect. In order to find the appropriate direction for this change, we have

estimated the own and cross-price elasticities of demand for the three main ticket types.

The number of available observations forces us to rely on some simplifying assumptions

and to estimate a simple model structure. Although this is probably not the best option,

it provides useful insights into demand behaviour. The model has been estimated only

for underground data in order to avoid counting bus transfers twice. Pensioner

concession trips have been excluded due to their different sensitivity to factors

influencing demand.

The model has been specified as in the aggregate equation, i.e. the demand equations

have been estimated in levels, using a double-log form and according to a partial

adjustment model. The model consists of three equations, one for each ticket type

(single, multi-ride and travel card) and it can be expressed as follows:

),,,( tttitit derfhy =

Where:

i = single fare, multi-ride fare or travel card

yit= passenger trips with fare type i in period t

fit= real price of fare type i in period t

rt=route length in period t

et=employment level in period t

d t=dummy variable that reflects the change of fare structure in 1998

The explanatory variables included in the equation are those that proved significant in

the aggregate model. A dummy variable has been added to take into account the

unification of the multi-ride ticket for bus and underground in 1998, which implied a

substitution of single ordinary tickets with multi-ride tickets. The sample period is from

1990 to 2001, the starting year being that in which the travel card scheme could be

considered complete.

Figure 4 summarises the changes in fares in real terms for the three ticket types. The

introduction of the travel card was accompanied by a sharp increase in the price of

single ticket fares, which continued until 1992. In 1993, the pricing policy was reversed

22

and single fares decreased in real terms until 2000. Between 1987 and 1991, the prices

of the travel card and multi-ride tickets were held constant in monetary terms with a

break-even number of trips equal to 73 per month. This ratio fell to an equivalent of 59

in 1993 and thereafter it increased gradually until it reached 65 in 2001. As shown in

figure 5, passengers reacted strongly to the change in price level and structure. Trips

using travel cards show a continued increase since its introduction even after its relative

price had been raised.

60

80

100

120

140

160

180

200

87 88 89 90 91 92 93 94 95 96 97 98 99 00 01

SINGLE MULTI CARD

Figure 4. Fare index f or the three ticket ty pes (1987=100)

0

50000

100000

150000

200000

250000

300000

350000

87 88 89 90 91 92 93 94 95 96 97 98 99 00 01

CARD MULTI SINGLE

Figure 5. Underground passenger trips by ticket ty pe

23

The equation system has been estimated by applying the SUR method. We proceed

from the general to the restricted model by omitting those variables that were found to

be non-significant20. The final equation (table 7) shows a good overall level of fit and all

coefficients take the expected sign and are highly significant. In order to increase the

degrees of freedom it was assumed that the coefficient of the lagged dependent variable

takes the same value in the three equations. This constraint was not rejected by the data.

Table 7. Estimation results of underground demand according to ticket type. 1990-2001

Dependent variable: log(single trips) log(multi-ride trips) log(travel pass trips)

Explanatory variablesCoefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic

Constant term 9.211 7.87 9.147 8.92 -0.752 -0.85log(single fare) -1.481 -9.85 n.s. n.s.log(multi-ride fare) n.s. -1.074 -13.38 0.217 2.58log(pass fare) n.s. 0.313 2.46 n.s.log(network) 0.647 4.70 0.249 5.83 0.160 1.73log(trips(-1)) 0.507 22.16 0.507 22.16 0.507 22.16log(employment) n.s. n.s. 0.622 4.07Dummy98 -0.259 -5.65

Observations 12 12 12R-squared 0.984 0.974 0.994Adjusted R-squared 0.975 0.959 0.990S.E. of regression 0.049 0.018 0.021h-test 0.12 -1.50 -1.04

n.s. not significant

The results related to price elasticities with respect to different ticket types show that,

firstly, price sensitivity decreases as we move from single tickets to the travel pass. This

is a common result in the literature on that subject (de Rus 1990, Hensher 1998, Tegner

et al. 1998). Secondly, users are highly sensitive with respect to single and multi-ride

fares with elasticity values equal to –1.5 and –1.1, respectively. These values have been

estimated with a high degree of confidence and prove highly robust in the model

specification. It is difficult to compare these results with other empirical evidence given

the wide range of observed variation. Our results are in line with those obtained by

Tegner et al. (1998), de Rus (1990) and García-Ferrer et al. (2002) who also used time

20 The variables found to be non-significant are the following. In the single ticket equation: multi-ridefare, travel card price and employment level; in the multi-ride ticket equation: single fare andemployment level and in the travel pass equation: single fare and its own price. According to thelikelihood ratio test the restricted model was not rejected; the calculated chi-square was 6.33, while thetabulated value for 9 degrees of freedom at 5% significant level is 16.9.

24

series data. Tegner et al. (1998) estimated a price elasticity equal to –1.8 for single

ticket and -0.8 for pre-paid coupons. For a sample of Spanish cities, de Rus (1990)

obtained a price elasticity for single fares of between –0.73 and –1.16, and for multi-

ride tickets between –0.27 and –2.25. García-Ferrer at al (2002) have estimated demand

elasticity with respect to ticket type for the same area and period as ours. They present a

price elasticity for single fares that is similar for buses and the underground at around –

1.1, while for multi-ride tickets the elasticity is equal to –2.4 for the underground and –

0.5 for buses. However, Hensher (1998) reported much lower values for choice

elasticities using micro data.

Thirdly, no elasticity statistically different from zero could be identified for the use of

the travel pass with respect to its own price. This result is also found by García-Ferrer et

al. (2002) and can be explained by the fact that relatively low changes in the breakeven

ratio between the travel card and multi-ride tickets have little effect on travel card sales.

Regarding this, White (1981) provides some arguments for elasticity being lower than

single ticket elasticity. The main reason is that when the price of a travel card is raised,

travel card holders do not reduce their trip frequency unless the price rises above a

critical threshold at which the user switches to single tickets at a much lower trip rate.

Additionally, given that an increase in card price encourages less frequent users to

abandon purchasing cards, it can be expected that total trips made by travel cards are

less price sensitive than the number of card holders21. The empirical evidence available

for travel card price elasticity tends to support low values. White (1981) suggests

negligible or very low price elasticity for moderate increases in the price of travel cards.

Hensher (1998) estimates a value of –0.1 and Tegner et al. (1998) an elasticity of –0.2

for a monthly pass. However, there is also evidence of higher elasticities for travel

passes. Dargay and Pekkarinen (1997) obtained an elasticity equal to –0.5 in the short

run for bus trips in two regional areas of Finland. However, in this case, the authors

suggest that a possible explanation for the large elasticity found could be the small

market share of travel cards with respect to total bus trips. Preston (1997) in a literature

review reports a wide range of variation, from very inelastic demand (-0.10) to very

elastic demand (-2.40). The differences can be explained by the nature of the data, the

specification of the demand model, the characteristics of travel passes and the definition

of the dependent variable.

25

With respect to cross-elasticities, it can be observed that once the effects of the new

travel card scheme have been established, single ticket users are sensitive only to their

own price, whereas positive and significant cross-price effects appear between multi-

ride and travel card users.

The estimated own and cross-price elasticities suggest that there is scope for change in

relative fare prices if revenue needs to be increased. The negligible sensitivity with

respect to travel card prices supports moderate increases in their prices without a

significant effect on patronage. However, further increases in the price of single and

multi-ride tickets will probably affect demand negatively given the high estimated

elasticity values.

Finally, in order to check the robustness of the elasticities reported in table 7, it is

interesting to see that when multiplied by the share of each ticket type, they result in

values very similar to the aggregate underground elasticities obtained previously (table

5). The market shares are set to be equal to the average value in the sample (single ticket

9%, multi-ride ticket 31% and travel passes 50%). The overall price elasticity assuming

an equiproportional price variation is equal to –0.33 whereas the overall service

elasticity is equal to 0.24.

εf = -1.48*0.09 + (-1.07+0.31)*0.41 + 0.22*0.50 = -0.33

εs = 0.65*0.09 + 0.25*0.41 + 0.16*0.50 = +0.24

6. Conclusions

The results of the estimated demand equations prove that the introduction of an

integrated fare system for the whole region and the improvement in bus and

underground networks have succeeded in reversing the declining patronage trend of

public transport in Madrid. However, we should take into account the fact that the

economic recovery which started in 1985 also played a significant role in increasing

public transport use. This happens to be the case mainly for the underground with a 21 This result is found in Dargay and Pekkarinen (1997).

26

higher proportion of commuters among its users. In the long run, the introduction of a

travel card system has increased underground trips by nearly 15% and bus trips by more

than 7%. The positive impact of travel cards in Madrid is in line with that found in

German and Swiss cities although below that estimated for London. The results confirm

the essential role of an integrated fare scheme for those cities that seek to increase

public transport use.

According to the results, bus users are sensitive to price and quality variables but also to

the underground network, implying a substitution effect between these modes of

transport. This substitution relationship allows for a rationalisation of the bus network

without a negative impact on total patronage. On the other hand, underground users are

only sensitive to underground characteristics. This can be partially explained by the

predominance of commuter trips that place a high value on variables such as time and

reliability that cannot be matched by bus supply. It can also be concluded that the price

of petrol should not be ignored as an element of the pricing policy given the statistically

significant, although small, impact on bus demand.

However, the adoption of these transport policies tends to require an increase in

financial support that is not always available. In the case of Madrid, the high market

penetration of the low cost travel card has had a negative effect on company revenues,

as occurred in the five case studies (Hamburg, Munich, Rhein-Ruhr region, Vienna and

Zurich) analysed by Pucher and Kurth (1996). The estimation of separate equations for

different ticket types provides interesting implications for fare policy. Firstly, the results

confirm White’s suggestion that card holders do not seem to be very sensitive to

moderate increases in price. Secondly, given the high value of price elasticities with

respect to single and multi-ride fares and the low value for cross-elasticities, further

increases in the price of these tickets would generate a loss of patronage. Therefore, the

results support the argument that there is scope for a more efficient non-uniform pricing

policy with positive effects on demand while minimising the negative effects on

revenue.

The experience of Madrid shows that a declining trend in public transport ridership can

be reversed through an active public transport policy based on low cost travel passes

and improvements in the quality of services. The same result has been observed in other

27

European cities. Among these are the well known cases of Austrian, German and Swiss

cities reported by Pucher and Kurth (1996). Therefore, an integrated public transport

system seems to be an advisable policy. However, several drawbacks need

consideration. First, although it has been proved that the public transport policy

observed by CTM has achieved a significant increase in patronage, there is not enough

information to understand the impact on modal shifts from car to public transport, which

is, in fact, the main goal of the urban transport policy. Therefore, more information

would need to be available in order to evaluate the success of the policy implemented.

Second, those policies addressed to improve public transport service usually have a

significant impact on costs. Therefore, a careful evaluation of benefits and costs of such

policies is needed.

Acknowledgements

I am grateful to Carmen Sanz of the Consorcio de Transportes de Madrid who provided

me with the necessary data and valuable comments on the paper. I must also thank EMT

and Metro Madrid, the Madrid urban bus and underground operating companies for the

detailed information provided. I would like to thank Professor J.L. Raymond, J. Asensio

and two anonymous referees for their valuable suggestions and comments. This work

has benefited from a research grant from the Spanish I+D+I Program (SEC 2000 047 4).

Appendix

1. The characteristics of the travel card22

In 1987, the regional transport authority (CTM) developed an integrated ticket system

covering all modes of public transport in the region. The scheme is based on a monthly

travel card that allows for unlimited travel across the entire network. Concessionary

cards are available for young people (30% discount) and those over 65 (more than 70%

discount). An annual card was later introduced for ordinary and youth tickets for the

same price as 11 monthly passes. A zonal structure has been designed based on

concentric rings around the central area. The central area, zone A, corresponds to the

municipality of Madrid and is served by metro and urban bus companies. The rest of the

28

region is divided into five zones (B1,B2,C1,C2,C3) that are served by private bus

companies and suburban railways. The system was systematically extended to all the

municipalities in the Madrid region. In 1990, the fare scheme was available for more

than 95% of the inhabitants of the region and is presently being extended outside the

Madrid region.

Prices are set according to the number of zones travelled through. In 1987 the monthly

card for zone A was priced at an equivalent of 97 single trips using the next cheapest

fare for busses and 73 trips for the underground. This ratio was progressively reduced to

65 in 2001 for both modes of transport. This may appear to be a high price for the travel

card, but there are two factors to be considered; first, the cheapest fare is a single-mode

fare, so for those journeys that imply a transfer (even between two different buses

belonging to the same company) the break-even ratio is much lower, and second, the

price ratio is much more attractive for those travelling across more than one zone.

Moreover, this figure is similar to that observed in other cities. For instance, White

(1981) reports a mean value of 80 trips per month for most travel card holders in

Western European cities when different stages of a single journey are recorded as an

additional trip.

2. Fare structure on bus and underground services

Until 1986, underground and bus operators had a different fare structure and level. For

the bus operator there were four different types of fare: ordinary single ticket, multi-ride

ticket, annual and quarterly passes and a concessionary ticket. The multi-ride ticket is a

ten-trip ticket offering a discount of nearly 40% with respect to the ordinary single

ticket. The concessionary ticket is also a ten-trip ticket offering a discount of 90% with

respect to the single ticket. People eligible for the concessionary fare are those older

than 65 or those below that age who are disabled with a monthly income below €425

(2000). Tickets issued by the bus operator are valid for only one bus trip and do not

cover transfers between buses. For the underground, four ticket types were also in use:

single tickets, day return tickets, multi-ride tickets and monthly and annual passes. The

day return ticket was only in use until the end of 1987 and offered a 30% discount with

respect to the single ticket. The multi-ride ticket was introduced in 1985 as a ten-trip 22 For an explanation of concessionary fare policy and its design see Cos Blanco and Zaragoza 1990.

29

ticket but without any discount. It was not until August 1987 that a discount of 18% was

offered. From that date and until 1990, the price of the single ticket was increased while

the price of the ten-trip ticket was held constant in monetary terms; in 1990 the discount

reached 46%. Transfers in the underground network are not penalised.

In 1987, the integrated zonal fare system was introduced while the previous fare

structure for each mode of transport practically remained unchanged. In 1998, multi-

ride tickets for busses and the underground were unified and the same strip could be

used in either transport mode. However, transfers are still penalised when single and

multi-ride tickets are used. Finally, since 1990 all fares for both modes of transport have

been set at the same level.

3. Estimation results of the general model

Table A.1 Estimation results of the general model

Sample: 1979 2001Included observations: 23Total system (balanced) observations 46

Coefficient Std. Error t-Statistic Prob.BUS EQUATION (bus trips)

Constant term -5.988462 6.085580 -0.984041 0.3349log(bus fare) -0.217801 0.085670 -2.542333 0.0179

log(bus route length) 0.536775 0.094332 5.690294 0.0000log(underground route length) -0.222807 0.039314 -5.667429 0.0000

log(petrol price) 0.112044 0.045159 2.481122 0.0205Step dummy variable 0.027897 0.015385 1.813273 0.0823

log(bus trips(-1)) 0.335390 0.088716 3.780478 0.0009log(GDP) 0.062698 0.195106 0.321353 0.7507

log(suburbanisation) 0.246769 0.210944 1.169829 0.2536log(population) 0.804857 0.394465 2.040376 0.0525

log(employment) -0.024255 0.119689 -0.202649 0.8411

UNDERGROUND EQUATION (underground trips)Constant term -1.715508 10.48184 -0.163665 0.8714

log(underground fare) -0.321186 0.117775 -2.727108 0.0118log(bus route length) 0.185212 0.163810 1.130647 0.2694

log(underground route length) 0.180620 0.088823 2.033473 0.0532log(petrol price) 0.030213 0.083074 0.363689 0.7193

Step dummy variable 0.063938 0.026609 2.402844 0.0244log(bus trips(-1)) 0.351732 0.142053 2.476072 0.0207

log(GDP) 0.258348 0.270976 0.953400 0.3499log(suburbanisation) -0.415547 0.338206 -1.228680 0.2311

log(population) 0.316527 0.833484 0.379764 0.7075log(employment) 0.343237 0.188957 1.816478 0.0818

Determinant residual covariance 3.13E-09BUS EQUATIONR-squared 0.993956 Mean dependent var 13.08589Adjusted R-squared 0.988920 S.D. dependent var 0.084868S.E. of regression 0.008933 Sum squared resid 0.000958

30

h-Durbin -1.09UNDERGROUND EQUATIONR-squared 0.995599 Mean dependent var 12.88395Adjusted R-squared 0.991931 S.D. dependent var 0.136995S.E. of regression 0.012306 Sum squared resid 0.001817h-Durbin -2.30

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